MATH2089 is a Level II course which is available only to students for whom it is specifically required as part of their program. See the course overview below.

Units of credit: 6

Prerequisites: MATH1231 or MATH1241 or MATH1251 or DPST1014

Exclusions: BEES2041, CVEN2002, CVEN2702, MATH2099, MATH2301, MATH2801, MATH2859, MATH2901, ECON3209

Cycle of offering: Term 1 & 2 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course outline contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH2089, you can log into UNSW Moodle for this course.

Course description

This course gives an introduction to numerical methods and statistics essential in a wide range of engineering disciplines.

Numerical methods: Computing with real numbers. Numerical differentiation, integration, interpolation and curve fitting (regression analysis). Solution of linear and nonlinear algebraic equations. Matrix operations and applications to solution of systems of linear equations, elimination and tri-diagonal matrix algorithms. Introduction to numerical solution of ordinary and partial differential equations.

Statistics: Exploratory data analysis. Probability and distribution theory including the Binomial, Poisson and Normal distributions. Large sample theory including the Central Limit Theorem. Elements of statistical inference including estimation, confidence intervals and hypothesis testing. One sample and two-sample t-tests and F-tests. Simple and multiple linear regression and analysis of variance. Statistical quality control.

In each component, applications will be drawn from a variety of engineering disciplines. Matlab will be used extensively as a practical tool for both numerical and statistical computations and to illustrate theoretical concepts.